BDAMI 2024
IEEE Workshop on Big Data Analytics for Medical Imaging
In conjunction with IEEE Big Data 2024 Washington DC, USA
IEEE Workshop on Big Data Analytics for Medical Imaging
In conjunction with IEEE Big Data 2024 Washington DC, USA
SPECIAL ISSUE APPROVED!!!
"AI-guided Big Data Analytics for Medical Imaging" on Multimedia Tools and Applications (MTAP) Springer.
Medical imaging technology has resulted in an unprecedented volume of diagnostic data from a variety of sources, including computed tomography (CT), magnetic resonance imaging (MRI), radiography, and more. However, the key problem is no longer just capturing high-resolution pictures, but also effectively understanding and extracting information from this vast amount of data. This is where Big Data analytics come in.
Big Data analytics in medical imaging seeks to extract important information, detect patterns and trends, and assist critical clinical choices. This approach, further helped by machine learning and deep learning techniques not only promises to improve diagnostic and treatment accuracy, but also to increase operational efficiency in healthcare services.
This workshop aims to attract the interest of Big Data and Medical Imaging researchers in developing novel methodologies, datasets, and approaches for diagnosis, patient treatment, medical image management optimization, and misinformation and disinformation detection.
BDAMI is aimed at different categories of professionals interested in harnessing the potential of Big Data in medical imaging.
Big Data Analytics for predictive diagnostics with medical imaging
Big Data Analytics for personalised treatment with medical imaging
Big Data Analytics for medical imaging supporting hospital workflow optimisation
Data Analytics for disease progression
Deepfakes detection on Medical Imaging
Misinformation and disinformation on medical imaging
Big Data Analytics for anomalies detection on medical imaging
Big Data Analytics for medical images enhancement and reconstruction
Big Data Analytics for noise reduction in medical images
Curated datasets for big data analytics in medical imaging
Synthetic datasets for big data analytics in medical imaging
September 20, 2024October 30, 2024: Due date for full workshop papers submission
Nov 10, 2024: Notification of paper acceptance to authors
Nov 21, 2024: Camera-ready of accepted papers
Dec 15, 2024: Workshop (Full Online)
Authors are invited to submit papers up to 10 pages references included (6 to 8 pages are recommended) in the IEEE 2-column format (IEEE Computer Society Proceedings Manuscript template) that can be found here.
All papers must be submitted via the conference submission system for the workshop.
Full registration of IEEE BigData 2024 is required for at least one of the authors for participating in the workshop.
Registration details and fees are available at the main conference website.
University of Salerno, Salerno, Italy
University of Electronic Science and Technology of China, Shenzhen, China
University of Salento, Lecce, Italy
Matteo Polsinelli, University of Salerno, Italy
Chiara Pero, University of Salerno, Italy
Imad Rida, Université de Technologie de Compiègne, France
Giorgio De Nunzio, University of Salento, Italy
Valerio De Luca, University of Salento, Italy
Antonella Calò, University of Salento, Italy
Saiyed Umer, Aliah University, India
Fabio Narducci, University of Salerno, Italy
Lucia Cimmino, University of Salerno, Italy
Lucia Cascone, University of Salerno, Italy
Andrea Loddo, University of Cagliari, Italy
For any information, please contact
Carmen Bisogni
University of Salerno
Salerno, Italy
cbisogni@unisa.it
The workshop will be held online. The link to connect is directly sent to the participants.
The time is referred to the main conference: US Eastern Standard Time.
"AI-guided Big Data Analytics for Medical Imaging"
The authors of chosen papers presented at BDAMI24 have the opportunity to submit an extended version of their contributions that incorporates both the reviewers' comments on their conference paper and the feedback obtained during the conference presentation. It is important to note that the extended version is expected to contain a significant scientific contribution, such as new algorithms, experiments, or qualitative/quantitative comparisons, and to not transfer large sections of the conference paper.
The call for paper is here.